Accurate Open-Loop Control of a Soft Continuum Robot Using Visually Learned Latent Dynamics

arXiv:2603.19655 · cs.RO, cs.SY, eess.SY · Submitted 2026-03-20 · Read on arXiv

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Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: Today's paper: "Accurate Open-Loop Control of a Soft Continuum Robot Using Visually Learned Latent Dynamics".

Dev: Accurate open-loop control of a soft continuum robot (SCR) from video-learned latent dynamics addresses the challenge of controlling complex, continuous systems without real-time camera feedback by leveraging interpretable latent representations.

Rosa: First, who's behind it and why it matters.

Paper summary: Rosa: So we're looking at this paper titled "Accurate Open-Loop Control of a Soft Continuum Robot Using Visually Learned Latent Dynamics," and its core thesis is that you can achieve open-loop control of a soft continuum robot without needing real-time camera feedback by using latent dynamics learned from video.

Dev: That's what it claims, Rosa; they are leveraging visual observations to learn these latent representations, which then allow for single-shooting optimal control in that latent space to follow image-specified waypoints.

Rosa: It matters because it tackles a real limitation: controlling complex systems like soft continuum robots without constant camera input is tough, and this approach uses interpretable latent representations to make it work reliably over long horizons.

Taro: I find the focus on interpretability interesting; if you can see what's happening in the latent space, that gives us a way to understand *why* the control is working, which is crucial when things go wrong outside of a perfect lab setting.

Dev: Exactly, Taro; they specifically use Visual Oscillator Networks from previous work augmented with an attention broadcast decoder to get those mechanistically interpretable 2D oscillator latents <ref:2603.19655#pg1,mechanistically interpretable 2D oscillator latents>.

Rosa: And the abstract highlights that they evaluate different dynamics models, including Koopman, MLP, and oscillator dynamics, each tested with and without this attention broadcast decoder to see which one performs best for image-space tracking error reduction.

Taro: It sounds like they are systematically comparing how different dynamical models handle the mapping from visual input to control action in this context.

Dev: Right, so the paper claims that the attention broadcast decoder based models, specifically Von and ABCD-based Koopman models, consistently reduce image-space tracking errors compared to other setups.

Rosa: That suggests that this specific combination of latent dynamics learning and decoder architecture is what makes them most effective for open-loop control tasks.

Taro: So the implication here is that explicit latent dynamical models can indeed support stable and accurate long-horizon open-loop control of soft continuum robots without needing camera feedback, which addresses a gap they pointed out previously.

Dev: Precisely, and they show that this method works even when targets need to come from unseen images or be derived artificially from user input for simulation purposes.

Conclusion: Rosa: Looking at the title, "Accurate Open-Loop Control of a Soft Continuum Robot Using Visually Learned Latent Dynamics," it really captures the essence: they figured out how to get precise control without needing constant visual input by learning a latent structure from video.

Dev: I think the authors, Krauss and colleagues, have shown that you don't necessarily need direct feedback from the camera for long-horizon tasks if you build a strong enough model of the system’s hidden dynamics in latent space.

Rosa: It means that for field robotics or remote operations where constant visual monitoring isn't feasible, this method provides a way to pre-program or plan trajectories based on visual context, which is a significant step forward in autonomy.

Taro: The implication for real-world deployment is that we could design SCRs that can follow complex paths dictated by an initial image and then execute those movements autonomously over time without needing continuous vision processing overhead.

Dev: And from an engineering standpoint, the work suggests that if you have a good latent model, you can formulate the control problem entirely in this latent space, which simplifies things immensely compared to trying to manage high-dimensional real-time visual inputs directly.

Rosa: So, in simple terms, they've shown a way to use learned representations of motion from video to guide the robot through a path without needing the camera running continuously during the actual movement.

Taro: That moves us closer to systems that can operate in environments where full-state feedback is limited, which is exactly what they mentioned as a challenge in their paper.

Dev: And they address the issue of targets potentially coming from unseen images or simulation, making it applicable beyond just testing in a controlled lab setting.

Rosa: It’s about moving towards more robust control schemes for soft robots in remote settings by grounding the control strategy in learned latent dynamics derived from vision.

Department of Advanced Interdisciplinary Studies, The University of Tokyo · Institute of Assembly Technology and Robotics, Leibniz University Hannover

cs.RO, cs.SY, eess.SY

Submitted: 2026-03-20

Updated: 2026-10-02

Comments: Accepted for IEEE Conference on Decision and Control 2026

Code: https://github.com/UThenrik/visual

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 83/100

The gist: Accurate open-loop control of a soft continuum robot (SCR) from video-learned latent dynamics addresses the challenge of controlling complex, continuous systems without real-time camera feedback by

Key concepts

Latent Dynamics
This refers to a mathematical model that describes how a robot's complex physical state (like its shape and movement) evolves over time, but instead of using raw visual data, it uses a compressed, meaningful representation (latent coordinates) learned from video. This latent space captures the essential dynamics of the soft robot's motion.
Visual Oscillator Networks (VONs)
VONs are a specific type of latent dynamical model used to learn these robot dynamics. They model the system's behavior using an equation similar to a physical oscillator, which helps capture the continuous, oscillatory nature of soft continuum robot movements effectively.
Open-Loop Control
Open-loop control means planning and executing a complete sequence of actions (a trajectory) in advance without needing immediate feedback from sensors during execution. In this context, the system uses its learned latent model to predict the entire future movement based on a desired path.
Attention Broadcast Decoder (ABCD)
The ABCD is a component added to the VON model that helps translate the abstract latent states back into meaningful visual outputs or image observations. It ensures that the predicted latent dynamics are consistent with what would be seen in an actual video frame.

Terminology

Summary

Accurate open-loop control of a soft continuum robot (SCR) from video-learned latent dynamics addresses the challenge of controlling complex, continuous systems without real-time camera feedback by leveraging interpretable latent representations. The core finding is that explicit latent dynamical models, specifically Visual Oscillator Networks (VONs) augmented with an attention broadcast decoder (ABCD), enable reliable long-horizon open-loop control of SCRs by mapping image-specified waypoints to model-specific latent states.

How it works

The method utilizes an encoder–dynamics–decoder model based on [2] and [13] to learn latent dynamics from video observations. For an image observation at step (i), the encoder produces latent coordinates, and the dynamics model predicts the next latent coordinates and velocities under input:

(zˆ(i+1), ˆz˙(i+1)) = fdyn(z(i), z˙(i), u(i)).

This is decoded to the next image observation: oˆ(i+1) = φ−1 (zˆ(i+1)). The full latent state is defined as ξ(i) = [z(i)⊤, z˙(i)⊤]⊤.

Latent Dynamical Models Compared

Three latent dynamical models are compared for learning the dynamics:

  1. Koopman: This model uses a transition matrix A for the latent state update: ξ(i+1) = Aξ(i) + B(u(i)).

  2. MLP: Providing a flexible baseline, an MLP predicts the next latent velocity as z˙(i+1) = fMLP(ξ(i)) + B(u(i)), from which the next latent coordinate is obtained by integration to ensure kinematic consistency.

  3. Oscillator network: The latent coordinates follow the equation of motion Mz¨ + Dz˙ + K(z − z0) = B(u), where z0 is an optional, learnable non-zero rest position used by VONs. This system is integrated using a symplectic Euler scheme with implicit damping to obtain the next state and velocity.

Model Refinements for Control

To improve suitability for open-loop control and extending on previous work, the dynamic single-step losses are replaced by multi-step losses over an H-step rollout, where H increases over training epochs. The loss functions include:

(10) Latent dynamical consistency loss:

L(H)d = 1/N X N n=1 1/H X H h=1 MSE(φ−1(zˆ(n,h)), o(n,h))

(11) Dynamic reconstruction loss:

L(H)z = 1/N X N n=1 1/H X H h=1 MSE(zˆ(n,h), z(n,h)) + MSE∆t · [ˆz˙(n,h), ∆t · z˙(n,h)]

Additionally, an adjusted rest-state loss (instead of a steady-state loss) is used to enforce that the rest state orest encodes to z0 and stays at equilibrium under rest actuation urest. For VONs specifically, mean correction is applied in the KL term:

L(z0)KL = -1/2N X N i=1 X j 1+log(σ(i)j) 2−(µ(i)j −z0,j) 2−(σ(i)j) 2

Predictive Control in Latent Space

The control problem is formulated as a discrete-time single-shooting open-loop optimal control problem over the control sequence u(0:T−1) subject to the learned latent dynamics (z(i+1), z˙(i+1)) = fdyn(z(i), z˙(i), u(i)). Targets are specified as one or more observations, and latent targets are defined as z∗k = φ(o∗k). The active target index k(i) is chosen as either the next or closest target from the current state index i.

Improvements for AI systems

Here are the specific improvements to AI systems derived from this paper, and what those improved systems can achieve:

  1. A new class of control architectures capable of performing open-loop control on complex, high-dimensional physical systems (like Soft Continuum Robots or other continuous deformation robots) directly from visual data without requiring real-time camera feedback.

  2. The integration of mechanistically interpretable latent dynamics models (specifically Visual Oscillator Networks - VONs coupled with an Attention Broadcast Decoder - ABCD) into deep learning frameworks for robot control. This moves beyond black-box latent representations to models where the learned states have physical meaning (e.g., representing oscillation modes).

  3. An SCR Live Simulator capable of generating interactive, image-specified target trajectories (static, dynamic, and extrapolated) and mapping them directly to model-specific latent waypoints. This allows for offline design of complex control tasks that can be tested on real hardware or used to train the control policy without requiring continuous visual input during the optimization phase.

  4. A robust open-loop optimal control framework implemented in latent space, utilizing a single-shooting method with a comprehensive cost function that penalizes:

  5. Tracking errors along a sequence of intermediate waypoints,

  6. Waypoint-exact accuracy at multiple distributed target points within the trajectory horizon, and

  7. Terminal state accuracy (both position and velocity). This framework allows the system to execute long-horizon maneuvers (like reaching far waypoints or performing complex dynamic sequences) autonomously based solely on an initial latent state and a pre-defined control sequence, effectively bypassing the need for continuous sensory feedback during execution.

  8. The ability for these learned models to exhibit stable physical behaviors under stress tests, including:

  9. Accurate static holding of target configurations (maintaining pressure equilibrium).

  10. Stable extrapolation into unseen states (e.g., ramp-up to pressures outside the training range) while maintaining plausible physical dynamics (like stable oscillations or damping forces).

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